RNAseq analysis
Miles Smith
07 February, 2020
| disease_class | n | percent |
|---|---|---|
| Control | 137 | 7.71% |
| SLE | 1640 | 92.29% |
Differential Gene Expression
| comparison | up | up % | down | down % | mean count cutoff | low counts | low counts % |
|---|---|---|---|---|---|---|---|
| SLE vs Control | 6628 | 13.34 % | 5091 | 10.25 % | 0 | 19195 | 38.63 % |
| Note: | |||||||
|
49690 with nonzero total read count Outliers: 0 |
25 genes with largest negative log-fold change in experimental group
| gene | log2FoldChange | lfc standard error | pvalue* | padj† |
|---|---|---|---|---|
| DDX3Y | 4.7 | 0.40 | 0.0e+00 | 1.9e-23 |
| MT-RNR2 | 4.6 | 0.23 | 0.0e+00 | 2.2e-08 |
| HBB | 4.3 | 0.22 | 2.0e-04 | 0.00094 |
| MT-RNR1 | 4.3 | 0.22 | 0.0e+00 | 4.6e-11 |
| TXLNGY | 4.3 | 0.30 | 0.0e+00 | 6.8e-42 |
| EIF1AY | 4.0 | 0.55 | 2.6e-04 | 0.0012 |
| KDM5D | 3.4 | 0.19 | 0.0e+00 | 1.7e-70 |
| AC108065.1 | 3.1 | 0.74 | 5.3e-04 | 0.0022 |
| RAB17 | 2.4 | 0.34 | 0.0e+00 | 1.9e-10 |
| MTCO1P40 | 2.3 | 0.52 | 8.1e-04 | 0.0032 |
| MTND1P23 | 1.9 | 0.54 | 6.1e-05 | 0.00033 |
| A4GALT | 1.6 | 0.23 | 0.0e+00 | 1.3e-11 |
| PLD5 | 1.6 | 0.30 | 0.0e+00 | 1.7e-08 |
| AC006065.5 | 1.5 | 0.29 | 1.6e-06 | 1.2e-05 |
| BCORP1 | 1.4 | 0.29 | 6.7e-06 | 4.4e-05 |
| SNORD95 | 1.4 | 0.24 | 0.0e+00 | 4.8e-09 |
| AC007368.1 | 1.3 | 0.29 | 1.0e-07 | 1.1e-06 |
| AP001329.1 | 1.3 | 0.26 | 2.5e-06 | 1.8e-05 |
| SNORD13 | 1.3 | 0.17 | 0.0e+00 | 1.3e-15 |
| SNORD33 | 1.3 | 0.19 | 2.6e-04 | 0.0012 |
| SNORD55 | 1.3 | 0.25 | 0.0e+00 | 9.6e-08 |
| MTND4P12 | 1.2 | 0.18 | 0.0e+00 | 4.1e-11 |
| NOVA1 | 1.2 | 0.30 | 2.0e-04 | 0.00093 |
| RNU6ATAC | 1.2 | 0.18 | 0.0e+00 | 5.7e-11 |
| SNX18P13 | 1.2 | 0.17 | 0.0e+00 | 1e-11 |
| * Wald test p-values | ||||
| † Benjamini–Hochberg adjusted value |
25 genes with largest positive log-fold change in experimental group
| gene | log2FoldChange | lfc standard error | pvalue* | padj† |
|---|---|---|---|---|
| RN7SKP71 | 7.4 | 0.62 | 0.0e+00 | 2.4e-36 |
| RN7SL5P | 7.3 | 0.55 | 0.0e+00 | 3.2e-40 |
| OTOF | 4.5 | 0.25 | 0.0e+00 | 3.1e-73 |
| NKAIN1 | 3.9 | 0.71 | 9.6e-05 | 0.00049 |
| PCSK9 | 3.9 | 0.71 | 1.5e-05 | 9e-05 |
| ADAMTS2 | 3.8 | 0.34 | 0.0e+00 | 9e-33 |
| H3F3AP6 | 3.8 | 0.91 | 5.2e-03 | 0.016 |
| RN7SKP203 | 3.8 | 0.45 | 0.0e+00 | 7.7e-19 |
| PAX3 | 3.0 | 0.48 | 0.0e+00 | 4.1e-07 |
| LINC01388 | 2.9 | 0.63 | 6.5e-04 | 0.0026 |
| SLC8A2 | 2.9 | 0.29 | 7.4e-05 | 0.00039 |
| IFI44L | 2.8 | 0.18 | 0.0e+00 | 5.9e-54 |
| Z84484.1 | 2.7 | 0.55 | 1.5e-04 | 0.00073 |
| IFI27 | 2.6 | 0.23 | 0.0e+00 | 6.6e-28 |
| RN7SL396P | 2.6 | 0.53 | 0.0e+00 | 8.9e-09 |
| SIGLEC1 | 2.6 | 0.17 | 0.0e+00 | 2.3e-50 |
| USP18 | 2.6 | 0.16 | 0.0e+00 | 4.6e-56 |
| AL592158.1 | 2.5 | 0.26 | 0.0e+00 | 1e-20 |
| RSAD2 | 2.5 | 0.16 | 0.0e+00 | 8.5e-55 |
| AC068299.2 | 2.4 | 0.19 | 0.0e+00 | 1.2e-34 |
| HSPB9 | 2.4 | 0.61 | 9.1e-03 | 0.026 |
| LINC00487 | 2.4 | 0.23 | 0.0e+00 | 2.7e-27 |
| PGF | 2.4 | 0.25 | 0.0e+00 | 6.8e-15 |
| RN7SL713P | 2.4 | 0.41 | 0.0e+00 | 3.2e-07 |
| 7SK | 2.3 | 0.25 | 0.0e+00 | 4.7e-20 |
| IFI44 | 2.3 | 0.15 | 0.0e+00 | 5.2e-53 |
| RN7SKP176 | 2.3 | 0.87 | 9.0e-05 | 0.00046 |
| USP41 | 2.3 | 0.27 | 0.0e+00 | 5.1e-27 |
| * Wald test p-values | ||||
| † Benjamini–Hochberg adjusted value |
Expression of top class DE genes by class
Expression of all top DE genes
Volcano plot of gene expression
## Warning: Removed 19374 rows containing missing values (geom_point).
Expression of selected interferon-stimulated genes
Expression of individual genes in the M1.2, M3.4, and M5.12 gene modules
Sample Clustering
Using gene expression distance, a sample adjacency matrix was calculated from a shared nearest neighbor graph. This, in turn, was used to cluster the samples, with the optimal clustering resolution being determined by silhouette width at multiple resolutions.
Clustering and variation of samples in reduced dimensional space
PCA
PCA, by disease classification
PCA, by plate
PCA, by initial sample concentration
PCA, by Leiden cluster
3D PCA, colored by Leiden cluster
UMAP
UMAP, by disease classification
UMAP, by plate
UMAP, by initial sample concentration
UMAP, by Leiden cluster
UMAP, in 3D, colored by Leiden cluster
Module scores
SLE Interferon module scores
SLE Inflammatory module scores
Low-density granulocyte module scores
SLE Modules with an annotated associated pathway
All SLE modules
Viral trancription
Samples were examined for transcripts from the following viruses: HSV-1, HSV-2, VZV, EBV, EBV-2, HCMV, HHV7, KSHV, Adenovirus 2, B19, HIV-1, HBV, HPV-2, HPV-16, JCV, and MCV-1
Detected viral transcription and Interferon module scores
Detected viral transcription and Inflammatory module scores
WGCNA
Expression similarity dendrogram
Expression of identified modules
By cluster
By disease classification
WGCNA eigenvalues
Gene ontology GSEA of WGCNA modules
Use of WGCNA modules in classifying clusters.
The module eigengene scores were used to train a random forest model with repeated cross validation to predict cluster identity.
The model was trained with 75% of the dataset and tested on 25%.
## Confusion Matrix and Statistics
##
## Reference
## Prediction 1 2 3
## 1 241 18 4
## 2 7 156 0
## 3 0 0 3
##
## Overall Statistics
##
## Accuracy : 0.9324
## 95% CI : (0.9044, 0.9543)
## No Information Rate : 0.5781
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.8624
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: 1 Class: 2 Class: 3
## Sensitivity 0.9718 0.8966 0.428571
## Specificity 0.8785 0.9725 1.000000
## Pos Pred Value 0.9163 0.9571 1.000000
## Neg Pred Value 0.9578 0.9323 0.990610
## Prevalence 0.5781 0.4056 0.016317
## Detection Rate 0.5618 0.3636 0.006993
## Detection Prevalence 0.6131 0.3800 0.006993
## Balanced Accuracy 0.9251 0.9346 0.714286The relative importance of each eigengene in classification:
## parRF variable importance
##
## variables are sorted by maximum importance across the classes
## only 20 most important variables shown (out of 31)
##
## 1 2 3
## MEmagenta 28.659 29.246 6.7112
## MEtan 25.455 24.937 8.3923
## MEgrey60 17.545 22.498 0.9272
## MElightcyan 15.667 18.279 11.0346
## MEturquoise 13.192 16.259 5.4120
## MEbrown 15.646 13.757 6.5662
## MEblue 13.812 14.841 3.2210
## MEgreen 13.781 14.264 9.8092
## MEpink 12.704 8.216 11.6982
## MEskyblue 8.591 12.589 5.3575
## MEorange 12.200 12.050 0.9116
## MElightyellow 12.189 4.574 3.0055
## MEdarkred 9.118 11.943 0.6649
## MEcyan 9.559 11.696 5.4114
## MEdarkgrey 11.270 8.918 5.2972
## MEmidnightblue 10.910 6.147 2.8580
## MEpurple 8.720 10.029 4.5571
## MEdarkgreen 9.573 9.954 4.3913
## MEroyalblue 9.516 3.773 3.1790
## MEdarkorange 9.432 6.597 1.0613Use of WGCNA modules in labeling disease class.
The module eigengene scores were used to train a random forest model with repeated cross validation to predict cluster identity.
The model was trained with 75% of the dataset and tested on 25%.
## Confusion Matrix and Statistics
##
## Reference
## Prediction Control SLE
## Control 14 5
## SLE 24 387
##
## Accuracy : 0.9326
## 95% CI : (0.9046, 0.9544)
## No Information Rate : 0.9116
## P-Value [Acc > NIR] : 0.0703022
##
## Kappa : 0.4594
##
## Mcnemar's Test P-Value : 0.0008302
##
## Sensitivity : 0.36842
## Specificity : 0.98724
## Pos Pred Value : 0.73684
## Neg Pred Value : 0.94161
## Prevalence : 0.08837
## Detection Rate : 0.03256
## Detection Prevalence : 0.04419
## Balanced Accuracy : 0.67783
##
## 'Positive' Class : Control
## The relative importance of each eigengene in classification:
## parRF variable importance
##
## only 20 most important variables shown (out of 31)
##
## Overall
## MEtan 31.387
## MEgreenyellow 12.638
## MEdarkgrey 8.846
## MEcyan 8.583
## MEsteelblue 8.043
## MEpaleturquoise 6.910
## MEturquoise 6.614
## MEdarkgreen 6.330
## MEpink 5.854
## MElightcyan 5.725
## MEwhite 5.389
## MEdarkorange 5.332
## MEdarkturquoise 5.256
## MEred 4.906
## MEskyblue 4.898
## MEdarkred 4.789
## MEgrey60 4.616
## MEmagenta 4.274
## MEmidnightblue 3.817
## MEblack 3.773Data processing of RNAseq data
Overview of sample characteristics:
## 1777 samples selected for analysis## 1774 files found## All files match a metadata entry.## A count file was not found for the following selected samples:## [1] "AA00393" "AA00150_2" "AA03681_2"Sample QC filtering
Samples with a PC1 > 2 (the red lines) and/or PC2 zscore > 2.5 (the green lines) were dropped from analysis.
| sample_name | disease_class | project | run_id | sample_alias | sex | race_code | initial_concentration_ng_ul | final_concentration_ng_ul | rin |
|---|---|---|---|---|---|---|---|---|---|
| AA02818 | Control | DxTerity | S4_004_1 | none | Female | [EA] | 1.518 | 1.518 | 6.1 |
| AA02024 | SLE | DxTerity | S4_004_1 | S00898-V08157 | Female | [EA] | 2.863 | 2.863 | 7.4 |
| AA02588 | SLE | DxTerity | S4_004_1 | none | Female | [EA] | 27.140 | 3.360 | 8.4 |
| AA03427 | SLE | DxTerity | S4_004_1 | S00898-V09086 | Female | [EA] | 14.853 | 14.853 | 7.8 |
| AA01977 | SLE | DxTerity | S4_004_1 | S00891-V08104 | Female | [EA] | 1.511 | 1.511 | 7.8 |
| AA03811 | SLE | DxTerity | S4_004_1 | S00096-V09475 | Female | [EA] | 2.775 | 2.775 | 5.1 |
| AA03609 | SLE | DxTerity | S4_004_1 | S00909-V09268 | Female | [AA] | 1.602 | 1.602 | 8.0 |
| AA02925 | SLE | DxTerity | S4_004_1 | S00582-V08574 | Female | [EA] | 6.603 | 6.603 | 5.9 |
| AA03322 | SLE | DxTerity | S4_004_2 | S00577-V08979 | Female | [AA] | 37.535 | 5.712 | 6.7 |
| AA04331 | SLE | ABC | S4_004_4 | ABC-39-V-SCR | Female | [AA] | 14.243 | 14.243 | 8.1 |
| AA04332 | SLE | ABC | S4_004_4 | ABC-40-V-01 | Female | [EA] | 2.824 | 2.824 | 9.8 |
| AA04358 | SLE | ABC | S4_004_4 | ABC-54-V-SCR | Female | [EA] | 4.479 | 4.479 | 10.0 |
| AA04365 | SLE | ABC | S4_004_4 | ABC-57-V-01 | Male | [EA] | 7.621 | 7.621 | 9.8 |
| AA04370 | SLE | ABC | S4_004_4 | ABC-57-V-07 | Male | [EA] | 10.127 | 10.127 | 9.6 |
| AA04444 | SLE | ABC | S4_005_1 | ABC-54-V-07 | Female | [EA] | 7.359 | 7.359 | 7.0 |
| AA01979 | SLE | DxTerity | S4_005_1 | S00158-V08102 | Female | [EA][H] | 8.574 | 8.574 | 6.8 |
| AA02257 | SLE | DxTerity | S4_005_1 | none | Female | [EA][H] | 3.485 | 3.485 | 7.1 |
| AA02583 | SLE | DxTerity | S4_005_1 | none | Female | [EA][H] | 7.620 | 7.620 | 7.0 |
| AA01979_2 | SLE | DxTerity | S4_005_2 | S00158-V08102 | Female | [EA][H] | 7.571 | 7.571 | 6.8 |
| AA02583_2 | SLE | DxTerity | S4_005_2 | none | Female | [EA][H] | 7.870 | 7.870 | 7.0 |
| AA04567 | SLE | ABC | S4_006_1 | ABC-54-V-05 | Female | [EA] | 3.105 | 3.105 | 6.9 |
| AA04517 | SLE | ABC | S4_006_1 | ABC-54-V-06 | Female | [EA] | 4.136 | 4.136 | 7.9 |
| AA04584 | SLE | DxTerity | S4_006_2 | S00211-V07644 | Male | [EA] | 5.920 | 5.920 | NA |
| AA00722 | SLE | DxTerity | S4_006_2 | S00429-V06264 | Female | [A] | 7.940 | 7.940 | NA |
| AA02938 | SLE | DxTerity | S4_006_2 | S00627-V08586 | Male | [AA] | 1.860 | 1.860 | NA |
| AA00961 | SLE | DxTerity | S4_006_3 | S00477-V06702 | Female | [EA] | 19.500 | 19.500 | 6.0 |
| AA01057 | SLE | DxTerity | S4_006_3 | S00477-V06843 | Female | [EA] | 11.300 | 11.300 | 6.3 |
| AA01455 | SLE | DxTerity | S4_006_3 | S00675-V07447 | Female | [EA] | 69.200 | 69.200 | 6.8 |
| AA01842 | SLE | DxTerity | S4_006_3 | S00323-V07949 | Female | [EA] | 29.000 | 29.000 | 6.8 |
| AA00420 | SLE | DxTerity | S4_006_4 | S00275-V05407 | Female | [EA] | 1.680 | 1.680 | 6.6 |
| AA03584 | SLE | OCRD | S4_007_1 | S00052-V09246 | Female | [AI] | 54.031 | NA | NA |
| AA03287 | SLE | OCRD | S4_007_1 | S00609-V08946 | Female | [EA] | 8.303 | NA | NA |
| AA00816 | SLE | OCRD | S4_007_1 | S00609-V06447 | Female | [EA] | 17.051 | NA | NA |
| AA00634 | SLE | OCRD | S4_007_1 | S00609-V06107 | Female | [EA] | 9.655 | NA | NA |
| AA00032 | SLE | OCRD | S4_007_3 | S00158-V03597 | Female | [EA][H] | 2.818 | NA | NA |
| AA01333 | SLE | OCRD | S4_007_3 | S00158-V07271 | Female | [EA][H] | 3.349 | NA | NA |
| AA02944 | SLE | OCRD | S4_007_3 | S00158-V08593 | Female | [EA][H] | 2.799 | NA | NA |
| AA01842_2 | SLE | OCRD | S4_008_1 | S00323-V07949 | Female | [EA] | 31.474 | 31.474 | NA |
| AA00555 | SLE | OCRD | S4_008_1 | S00180-V05964 | Male | [EA] | 15.928 | 15.928 | NA |
| AA04628 | SLE | BLAST | S4_008_2 | BT-01-V-SCR | Female | [EA] | 13.851 | 13.851 | NA |
| AA04629 | SLE | BLAST | S4_008_2 | BT-01-V-BSL | Female | [EA] | 21.819 | 21.819 | NA |
| AA04630 | SLE | BLAST | S4_008_2 | BT-01-V-01 | Female | [EA] | 1.776 | 1.776 | NA |
| AA04636 | SLE | BLAST | S4_008_2 | BT-01-V-05 | Female | [EA] | 5.166 | 5.166 | NA |
| AA04637 | SLE | BLAST | S4_008_2 | BT-01-V-06 | Female | [EA] | 6.480 | 6.480 | NA |
| AA04773 | SLE | BLAST | S4_008_3 | BT-17-V-07 | Female | [EA] | 51.200 | 51.200 | NA |
| AA04782 | SLE | BLAST | S4_008_3 | BT-18-V-02 | Female | [EA] | 10.900 | 10.900 | NA |
| AA04784 | SLE | BLAST | S4_008_3 | BT-20-V-SCR | Female | [EA] | 11.400 | 11.400 | NA |
| AA04873 | SLE | BLAST | S4_008_4 | BT-30-V-SCR | Female | [AI] | 14.240 | 14.240 | NA |
| AA05003 | SLE | OCRD | S4_009_1 | S00577-V10295 | Female | [AA] | 6.755 | 6.755 | NA |
| AA00901 | SLE | OCRD | S4_009_2 | S00371-V06601 | Female | [EA] | 57.725 | 57.725 | NA |
| AA01442 | SLE | OCRD | S4_009_4 | S00829-V07429 | Female | [EA] | 71.940 | 71.940 | NA |
| AA01272 | SLE | OCRD | S4_009_4 | S00750-V07189 | Female | [EA][H] | 15.602 | 15.602 | NA |
Overall correlation between samples
Data quality
MA-plot
This MA plot is of shrunken log2 fold changes (using the apeglm package), in which actual fold changes are adjusted to compensate for the high variablity of genes with few counts.
Dots in red indicate a significant expression difference between Control and SLE. 
Dispersion estimates
Cluster resolution optimization
The samples are clustered using the Leiden algorithm on shared nearest neighbors calculated from sample distances. Since the clustering resolution is an emperical value that has to be determined for each dataset, a range of resolutions were tested and then the silhouette coefficient was calculated at each resolution. A higher coefficient means better intracluster simliarity and intercluster differences. 
Sample Sanity check
To determine if there is a gross mixup of samples, examine the expression of sex-specific transcripts to ensure that only female-subject samples express “XIST” and male-subject samples "DDX3Y:
These appear to be correct.
## Warning in system("timedatectl", intern = TRUE): running command 'timedatectl' had status 1## ─ Session info ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## setting value
## version R version 3.6.1 (2019-07-05)
## os Debian GNU/Linux 9 (stretch)
## system x86_64, linux-gnu
## ui X11
## language (EN)
## collate en_US.UTF-8
## ctype en_US.UTF-8
## tz Etc/UTC
## date 2020-02-07
##
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##
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